We propose to develop software for tree-based methods which implements research on biomedical applications not currently supported by commercial software. Examples include survival and longitudinal data analysis, generalized tree models, and clustering. Tree-based tools offer many advantages including easy interpretation since tree representations may mimic the way that many medical scientists think about data. Phase I research will focus on (1) analyzing user and software requirements, (2) creating an object-oriented design, (3) implementing this design for clustering and survival analysis, and researching the practical issues of (4) handling missing values using multiple imputation and (5) creating diagnostics for tree stability using the bootstrap. In Phase II, we will complete the coding and testing of software for survival analysis and clustering applications, and develop code for longitudinal data analysis and generalized tree models. We will create software that analysts find flexible and easy to use, enabling medical researchers to use tree-based tools to explore and understand data from a wide variety of applications. Additionally, the software will be supported in an integrated environment for data analysis, and permit analysts, consultants, and statistical researchers to extend the software to incorporate future innovations in recursive partitioning research. PROPOSED COMMERCIAL APPLICATIONS: This SBIR will result in a new software package which is inter- operable with existing S-Plus products. We expect this product to appeal to a wide market of users including data analysts, consultants, and researchers in disciplines as diverse as medicine, pattern recognition, data mining, and image analysis.